OpenRaiser/Intern-Atlas huggingface dataset:
Investigating the Feasibility of Virtual Reality for Emotion Regulation with Youth
Venue: arXiv cs.HC 2022
Year: 2022

The ability to regulate and cope with strong emotions is essential for maintaining our mental health and well-being. However, learning how to emotionally regulate can be a bit of a mystery since it is largely an invisible process and it can be difficult to conjure up strong emotions to practice regulating them. This is where virtual reality (or VR) comes in. VR is a computer-generated 3D environment where the user experiences a simulated world through 360 visuals, stereo audio, and 3D interaction with tracking sensors. VR is a very visceral experience that feels 'real' even though you know it isn't. If a virtual ball came flying at your head, you would duck! My past research shows that we can provide VR experiences that elicit strong emotional reactions so that people can practice coping and regulating their emotional responses. For example, I helped create a VR experience of being in nature and then going into space to see the Earth; it created the emotional reaction of awe and wonder that led to a deeper connection with our planet. This shows that emotional VR experiences can impact our emotions and behaviour both in VR and beyond. My research proposal is to investigate the feasibility of emotion regulation skills development in VR with teenagers. The idea is to simulate emotional experiences (like the 1st day of high school) as a means to develop emotion regulation skills so that they will be able to better cope with their emotions. I will lead the design, development, and evaluation of this VR experience and work directly with youth to meet their needs. This proof of concept prototype is the first step in developing a VR platform that provides youth with an effective way to regulate their emotions and improve their mental health from their own homes, which will lead to improvements in education, socio-emotional, and economic outcomes for youth in Canada and globally.
Advanced Audio Aid for Blind People
Venue: arXiv cs.HC 2022
Year: 2022

One of the most important senses in human life is vision, without it life is totally filled with darkness. According to WHO globally millions of people are visually impaired estimated there are 285 million, of whom some millions are blind. Unfortunately, there are around 2.4 million people are blind in our beloved country Pakistan. Human are a crucial part of society and the blind community is a main part of society. The technologies are grown so far to make the life of humans easier more comfortable and more reliable for. However, this disability of the blind community would reduce their chance of using such innovative products. Therefore, the visually impaired community believe that they are burden to other societies and they do not capture in normal activities separates the blind people from society and because of this believe did not participate in the normally tasks of society . The visual impair people mainly face most of the problems in this real-time The aim of this work is to turn the real time world into an audio world by telling blind person about the objects in their way and can read printed text. This will enable blind persons to identify the things and read the text without any external help just by using the object detection and reading system in real time. Objective of this work: i) Object detection ii) Read printed text, using state-of-the-art (SOTA) technology.
Web-based Database Courses E-Learning Application
Venue: arXiv cs.HC 2022
Year: 2022

This study was focused on the development of a web e-learning application for the database courses taken by Information Technology (IT) students at the College of Information and Communications Technology (CICT) of Bulacan State University (BulSU). The research methodology used in this project was the cross-sectional developmental approach. The Agile Software Development methodology was followed phase by phase, up to the development phase, to develop the system. It was used to produce the desired output rapidly while allowing users to go back through phases without finishing the whole cycle. The goal of this study was to create a web application that teaches Structured Query Language (SQL) in both the MySQL and SQL Server approaches. The application contains quizzes and examinations to allow self-assessment of learning. Additionally, an Entity Relationship Diagram (ERD) simulation was included to provide ERD creations in a drag-and-drop fashion. This study was evaluated using ISO/IEC 25010 software quality evaluation criteria. The study's overall mean was 4.24, 4.41, and 4.33, all with the descriptive meaning of "Very Good," which showed that the system performed its necessary functions as perceived by students, faculty members, and experts, respectively. In summary, the e-learning web application for database courses was fully developed. Moreover, the entity-relationship diagram was integrated well within the system and is accessible to the users. Lastly, respondents evaluated the developed web application using the ISO/IEC 25010 with an overall descriptive interpretation of "Very Good." For future developments of the study, an administrator panel may be developed to manage users and do other administrative tasks. Lastly, higher-order thinking skills questions on assessments and quizzes may be included.
Incentivising cooperation by rewarding the weakest member
Venue: arXiv cs.MA 2022
Year: 2022

Autonomous agents that act with each other on behalf of humans are becoming more common in many social domains, such as customer service, transportation, and health care. In such social situations greedy strategies can reduce the positive outcome for all agents, such as leading to stop-and-go traffic on highways, or causing a denial of service on a communications channel. Instead, we desire autonomous decision-making for efficient performance while also considering equitability of the group to avoid these pitfalls. Unfortunately, in complex situations it is far easier to design machine learning objectives for selfish strategies than for equitable behaviors. Here we present a simple way to reward groups of agents in both evolution and reinforcement learning domains by the performance of their weakest member. We show how this yields ``fairer'' more equitable behavior, while also maximizing individual outcomes, and we show the relationship to biological selection mechanisms of group-level selection and inclusive fitness theory.
A comparison between Automatically versus Manually Parallelized NAS Benchmarks
Venue: arXiv cs.DC 2022
Year: 2022

We compare automatically and manually parallelized NAS Benchmarks in order to identify code sections that differ. We discuss opportunities for advancing automatic parallelizers. We find ten patterns that pose challenges for current parallelization technology. We also measure the potential impact of advanced techniques that could perform the needed transformations automatically. While some of our findings are not surprising and difficult to attain -- compilers need to get better at identifying parallelism in outermost loops and in loops containing function calls -- other opportunities are within reach and can make a difference. They include combining loops into parallel regions, avoiding load imbalance, and improving reduction parallelization. Advancing compilers through the study of hand-optimized code is a necessary path to move the forefront of compiler research. Very few recent papers have pursued this goal, however. The present work tries to fill this void.
Time-Efficient Reward Learning via Visually Assisted Cluster Ranking
Venue: arXiv cs.LG 2022
Year: 2022

One of the most successful paradigms for reward learning uses human feedback in the form of comparisons. Although these methods hold promise, human comparison labeling is expensive and time consuming, constituting a major bottleneck to their broader applicability. Our insight is that we can greatly improve how effectively human time is used in these approaches by batching comparisons together, rather than having the human label each comparison individually. To do so, we leverage data dimensionality-reduction and visualization techniques to provide the human with a interactive GUI displaying the state space, in which the user can label subportions of the state space. Across some simple Mujoco tasks, we show that this high-level approach holds promise and is able to greatly increase the performance of the resulting agents, provided the same amount of human labeling time.
Experimental Observations of the Topology of Convolutional Neural Network Activations
Venue: arXiv cs.LG 2022
Year: 2022

Topological data analysis (TDA) is a branch of computational mathematics, bridging algebraic topology and data science, that provides compact, noise-robust representations of complex structures. Deep neural networks (DNNs) learn millions of parameters associated with a series of transformations defined by the model architecture, resulting in high-dimensional, difficult-to-interpret internal representations of input data. As DNNs become more ubiquitous across multiple sectors of our society, there is increasing recognition that mathematical methods are needed to aid analysts, researchers, and practitioners in understanding and interpreting how these models' internal representations relate to the final classification. In this paper, we apply cutting edge techniques from TDA with the goal of gaining insight into the interpretability of convolutional neural networks used for image classification. We use two common TDA approaches to explore several methods for modeling hidden-layer activations as high-dimensional point clouds, and provide experimental evidence that these point clouds capture valuable structural information about the model's process. First, we demonstrate that a distance metric based on persistent homology can be used to quantify meaningful differences between layers, and we discuss these distances in the broader context of existing representational similarity metrics for neural network interpretability. Second, we show that a mapper graph can provide semantic insight into how these models organize hierarchical class knowledge at each layer. These observations demonstrate that TDA is a useful tool to help deep learning practitioners unlock the hidden structures of their models.
Low-Rank Tensor Function Representation for Multi-Dimensional Data Recovery
Venue: arXiv cs.CV 2022
Year: 2022

Since higher-order tensors are naturally suitable for representing multi-dimensional data in real-world, e.g., color images and videos, low-rank tensor representation has become one of the emerging areas in machine learning and computer vision. However, classical low-rank tensor representations can only represent data on finite meshgrid due to their intrinsical discrete nature, which hinders their potential applicability in many scenarios beyond meshgrid. To break this barrier, we propose a low-rank tensor function representation (LRTFR), which can continuously represent data beyond meshgrid with infinite resolution. Specifically, the suggested tensor function, which maps an arbitrary coordinate to the corresponding value, can continuously represent data in an infinite real space. Parallel to discrete tensors, we develop two fundamental concepts for tensor functions, i.e., the tensor function rank and low-rank tensor function factorization. We theoretically justify that both low-rank and smooth regularizations are harmoniously unified in the LRTFR, which leads to high effectiveness and efficiency for data continuous representation. Extensive multi-dimensional data recovery applications arising from image processing (image inpainting and denoising), machine learning (hyperparameter optimization), and computer graphics (point cloud upsampling) substantiate the superiority and versatility of our method as compared with state-of-the-art methods. Especially, the experiments beyond the original meshgrid resolution (hyperparameter optimization) or even beyond meshgrid (point cloud upsampling) validate the favorable performances of our method for continuous representation.
Decision Market Based Learning For Multi-agent Contextual Bandit Problems
Venue: arXiv cs.MA 2022
Year: 2022

Information is often stored in a distributed and proprietary form, and agents who own information are often self-interested and require incentives to reveal their information. Suitable mechanisms are required to elicit and aggregate such distributed information for decision making. In this paper, we use simulations to investigate the use of decision markets as mechanisms in a multi-agent learning system to aggregate distributed information for decision-making in a contextual bandit problem. The system utilises strictly proper decision scoring rules to assess the accuracy of probabilistic reports from agents, which allows agents to learn to solve the contextual bandit problem jointly. Our simulations show that our multi-agent system with distributed information can be trained as efficiently as a centralised counterpart with a single agent that receives all information. Moreover, we use our system to investigate scenarios with deterministic decision scoring rules which are not incentive compatible. We observe the emergence of more complex dynamics with manipulative behaviour, which agrees with existing theoretical analyses.
Generalizing and Improving Jacobian and Hessian Regularization
Venue: arXiv cs.LG 2022
Year: 2022

Jacobian and Hessian regularization aim to reduce the magnitude of the first and second-order partial derivatives with respect to neural network inputs, and they are predominantly used to ensure the adversarial robustness of image classifiers. In this work, we generalize previous efforts by extending the target matrix from zero to any matrix that admits efficient matrix-vector products. The proposed paradigm allows us to construct novel regularization terms that enforce symmetry or diagonality on square Jacobian and Hessian matrices. On the other hand, the major challenge for Jacobian and Hessian regularization has been high computational complexity. We introduce Lanczos-based spectral norm minimization to tackle this difficulty. This technique uses a parallelized implementation of the Lanczos algorithm and is capable of effective and stable regularization of large Jacobian and Hessian matrices. Theoretical justifications and empirical evidence are provided for the proposed paradigm and technique. We carry out exploratory experiments to validate the effectiveness of our novel regularization terms. We also conduct comparative experiments to evaluate Lanczos-based spectral norm minimization against prior methods. Results show that the proposed methodologies are advantageous for a wide range of tasks.
xEM: Explainable Entity Matching in Customer 360
Venue: arXiv cs.AI 2022
Year: 2022

Entity matching in Customer 360 is the task of determining if multiple records represent the same real world entity. Entities are typically people, organizations, locations, and events represented as attributed nodes in a graph, though they can also be represented as records in relational data. While probabilistic matching engines and artificial neural network models exist for this task, explaining entity matching has received less attention. In this demo, we present our Explainable Entity Matching (xEM) system and discuss the different AI/ML considerations that went into its implementation.
AstroSLAM: Autonomous Monocular Navigation in the Vicinity of a Celestial Small Body -- Theory and Experiments
Venue: arXiv cs.RO 2022
Year: 2022

We propose AstroSLAM, a standalone vision-based solution for autonomous online navigation around an unknown target small celestial body. AstroSLAM is predicated on the formulation of the SLAM problem as an incrementally growing factor graph, facilitated by the use of the GTSAM library and the iSAM2 engine. By combining sensor fusion with orbital motion priors, we achieve improved performance over a baseline SLAM solution. We incorporate orbital motion constraints into the factor graph by devising a novel relative dynamics factor, which links the relative pose of the spacecraft to the problem of predicting trajectories stemming from the motion of the spacecraft in the vicinity of the small body. We demonstrate the excellent performance of AstroSLAM using both real legacy mission imagery and trajectory data courtesy of NASA's Planetary Data System, as well as real in-lab imagery data generated on a 3 degree-of-freedom spacecraft simulator test-bed.
A Dataset with Multibeam Forward-Looking Sonar for Underwater Object Detection
Venue: arXiv cs.CV 2022
Year: 2022

Multibeam forward-looking sonar (MFLS) plays an important role in underwater detection. There are several challenges to the research on underwater object detection with MFLS. Firstly, the research is lack of available dataset. Secondly, the sonar image, generally processed at pixel level and transformed to sector representation for the visual habits of human beings, is disadvantageous to the research in artificial intelligence (AI) areas. Towards these challenges, we present a novel dataset, the underwater acoustic target detection (UATD) dataset, consisting of over 9000 MFLS images captured using Tritech Gemini 1200ik sonar. Our dataset provides raw data of sonar images with annotation of 10 categories of target objects (cube, cylinder, tyres, etc). The data was collected from lake and shallow water. To verify the practicality of UATD, we apply the dataset to the state-of-the-art detectors and provide corresponding benchmarks for its accuracy and efficiency.
Why Are Conditional Generative Models Better Than Unconditional Ones?
Venue: arXiv cs.LG 2022
Year: 2022

Extensive empirical evidence demonstrates that conditional generative models are easier to train and perform better than unconditional ones by exploiting the labels of data. So do score-based diffusion models. In this paper, we analyze the phenomenon formally and identify that the key of conditional learning is to partition the data properly. Inspired by the analyses, we propose self-conditioned diffusion models (SCDM), which is trained conditioned on indices clustered by the k-means algorithm on the features extracted by a model pre-trained in a self-supervised manner. SCDM significantly improves the unconditional model across various datasets and achieves a record-breaking FID of 3.94 on ImageNet 64x64 without labels. Besides, SCDM achieves a slightly better FID than the corresponding conditional model on CIFAR10.
Kick-motion Training with DQN in AI Soccer Environment
Venue: arXiv cs.RO 2022
Year: 2022

This paper presents a technique to train a robot to perform kick-motion in AI soccer by using reinforcement learning (RL). In RL, an agent interacts with an environment and learns to choose an action in a state at each step. When training RL algorithms, a problem called the curse of dimensionality (COD) can occur if the dimension of the state is high and the number of training data is low. The COD often causes degraded performance of RL models. In the situation of the robot kicking the ball, as the ball approaches the robot, the robot chooses the action based on the information obtained from the soccer field. In order not to suffer COD, the training data, which are experiences in the case of RL, should be collected evenly from all areas of the soccer field over (theoretically infinite) time. In this paper, we attempt to use the relative coordinate system (RCS) as the state for training kick-motion of robot agent, instead of using the absolute coordinate system (ACS). Using the RCS eliminates the necessity for the agent to know all the (state) information of entire soccer field and reduces the dimension of the state that the agent needs to know to perform kick-motion, and consequently alleviates COD. The training based on the RCS is performed with the widely used Deep Q-network (DQN) and tested in the AI Soccer environment implemented with Webots simulation software.
GrannGAN: Graph annotation generative adversarial networks
Venue: arXiv cs.LG 2022
Year: 2022

We consider the problem of modelling high-dimensional distributions and generating new examples of data with complex relational feature structure coherent with a graph skeleton. The model we propose tackles the problem of generating the data features constrained by the specific graph structure of each data point by splitting the task into two phases. In the first it models the distribution of features associated with the nodes of the given graph, in the second it complements the edge features conditionally on the node features. We follow the strategy of implicit distribution modelling via generative adversarial network (GAN) combined with permutation equivariant message passing architecture operating over the sets of nodes and edges. This enables generating the feature vectors of all the graph objects in one go (in 2 phases) as opposed to a much slower one-by-one generations of sequential models, prevents the need for expensive graph matching procedures usually needed for likelihood-based generative models, and uses efficiently the network capacity by being insensitive to the particular node ordering in the graph representation. To the best of our knowledge, this is the first method that models the feature distribution along the graph skeleton allowing for generations of annotated graphs with user specified structures. Our experiments demonstrate the ability of our model to learn complex structured distributions through quantitative evaluation over three annotated graph datasets.
CUNI Non-Autoregressive System for the WMT 22 Efficient Translation Shared Task
Venue: arXiv cs.CL 2022
Year: 2022

We present a non-autoregressive system submission to the WMT 22 Efficient Translation Shared Task. Our system was used by Helcl et al. (2022) in an attempt to provide fair comparison between non-autoregressive and autoregressive models. This submission is an effort to establish solid baselines along with sound evaluation methodology, particularly in terms of measuring the decoding speed. The model itself is a 12-layer Transformer model trained with connectionist temporal classification on knowledge-distilled dataset by a strong autoregressive teacher model.
P2Net: A Post-Processing Network for Refining Semantic Segmentation of LiDAR Point Cloud based on Consistency of Consecutive Frames
Venue: arXiv cs.CV 2022
Year: 2022

We present a lightweight post-processing method to refine the semantic segmentation results of point cloud sequences. Most existing methods usually segment frame by frame and encounter the inherent ambiguity of the problem: based on a measurement in a single frame, labels are sometimes difficult to predict even for humans. To remedy this problem, we propose to explicitly train a network to refine these results predicted by an existing segmentation method. The network, which we call the P2Net, learns the consistency constraints between coincident points from consecutive frames after registration. We evaluate the proposed post-processing method both qualitatively and quantitatively on the SemanticKITTI dataset that consists of real outdoor scenes. The effectiveness of the proposed method is validated by comparing the results predicted by two representative networks with and without the refinement by the post-processing network. Specifically, qualitative visualization validates the key idea that labels of the points that are difficult to predict can be corrected with P2Net. Quantitatively, overall mIoU is improved from 10.5% to 11.7% for PointNet [1] and from 10.8% to 15.9% for PointNet++ [2].